Project Aim

Model Specification

Overview

  • State-level hierarchical generalized additive model (GAM) that models the prevalence of RR-TB positive cases per quarter among incident TB cases between 2014-2019

  • Fit smoothing functions to reduce the noise we were seeing in previous models

  • Models risk of positivity by characteristics of patient and municipality where they reside

    • Note: Between 2014-2019, ~3,300 cases diagnosed outside of patient’s state of residence; ~88,000 cases diagnosed outside patient’s municipality of residence
  • Separate models for new TB cases, re-entry cases, and relapsed cases

Set Up

result ~ s(state, bs = "re") + s(time) + s(time, by = state, id = 1) + age_cat + 
  hiv_status + sex + health_unit + bf_cat + urban_cat + has_prison
  • Random intercept for each state (patient state of residence)

  • A different smooth function for time by state with a shared smoothing parameter

  • Each state-level smoothing parameter varies around a grand smooth function for time to allow for pooling across states

  • Fixed effects for patient-level characteristics:

    • Age
    • HIV status
    • Sex
    • Level of health unit of diagnosis - Based on CNES merge
  • Fixed effects for municipality-level characteristics:

    • Urbanicity (cat) - Percent of the population in urban setting (2010 census)
    • Bolsa Familia coverage (cat) - Percent of the population benefiting from BF (BF: SAGICAD, 2018 - earliest year available; Denominator: 2010 Census)
    • Presence of prison during year (SISPEN)
    • FHS Coverage - Number of health teams per 4,000 people (Forthcoming)

Specifications

  • Model 1 - Adjusted:

    • Patient-level: HIV status, sex, age, health unit level
    • Municipality: BF coverage, urbanicity, presence of prison
  • Model 2 - Adjusted; Restricted > 2015

  • Run separately by case type (e.g. new, relapse, re-entry) and for all cases

Model Output

New cases

  • Model A - Adjusted:

    • Patient-level: HIV status, sex, age (cat), health unit
    • Municipality: Pct average BF coverage (cat), pct pop in urban area, presence of prison
  • Model B - Model A + interaction term (HIV, sex, age)

  • Model C - Model A, restricted to >2015

  • Time trends in RR-TB positivity (Model A - Adjusted):

    • 2017:
      • Average: 0.04
      • Range: 0.015 (Sao Paulo) - 0.089 (Maranhão)
      • São Paulo: 0.015
    • 2018:
      • Average: 0.03
      • Range: 0.012 (Rio Grande do Norte) - 0.068 (Maranhão)
      • São Paulo: 0.015
    • 2019:
      • Average: 0.03
      • Range: 0.01 (Sergipe) - 0.045 (Maranhão)
      • São Paulo: 0.015

Re-Entry Cases

Note: The following figures show the same model output, only the Y axis changes to show variation within each state.

Fixed Axes

Varied Axes

  • Time trends in RR-TB positivity (Model A - Adjusted):

    • 2017:
      • Average: 0.058
      • Range: 0.022 (Rio Grande do Norte) - 0.114 (Rio Grande do Sul)
      • São Paulo: 0.048
    • 2018:
      • Average: 0.059
      • Range: 0.024 (Rio Grande do Norte) - 0.141 (Amapá)
      • São Paulo: 0.042
    • 2019:
      • Average: 0.055
      • Range: 0.017 (Sergipe) - 0.217 (Amapá)
      • São Paulo: 0.0315

Relapse Cases

Note: The following figures show the same model output, only the Y axis changes to show variation within each state.

Fixed axes by state

Varied axes by state

  • Time trends in RR-TB positivity (Model A - Adjusted):

    • 2017:
      • Average: 0.063
      • Range: 0.036 (São Paulo) - 0.094 (Rio de Janeiro)
      • São Paulo: 0.036
    • 2018:
      • Average: 0.065
      • Range: 0.031 (Pernambuco) - 0.116 (Maranhão)
      • São Paulo: 0.04
    • 2019:
      • Average: 0.047
      • Range: 0.0132 (Pernambuco) - 0.109 (Maranhão)
      • São Paulo: 0.0311

All cases (e.g. Diagnoses types combined)

Going Forward: